Theses and Dissertations from UMD
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Item Monitoring Aboveground Biomass in Forest Conservation and Restoration Areas Using GEDI and Optical Data Fusion(2024) Liang, Mengyu; Duncanson, Laura I; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Forests play a critical role in the global carbon cycle by sequestering carbon in the form of aboveground biomass. Area-based conservation measures, such as protected areas (PAs), are a cornerstone conservation strategy for preserving some of the world's most at-risk forest ecosystems. Beyond PAs, tree planting and forest restoration have been lauded as solutions to combat climate change and criticized as ways for polluters to offset carbon emissions. Consistent monitoring and quantification of forest restoration can impact decisions on future restoration activities. In this dissertation, I utilized a fusion of remote sensing assets and a combination of remote sensing with impact assessment techniques, to obtain objective baseline information for reconstructing past forest biomass conditions, and for monitoring and quantifying the patterns and success of forest regrowth in areas that underwent different forest management interventions. This overarching research goal is approached in three studies corresponding to chapters 2-4. In chapter 2, PAs’ effectiveness in storing biomass carbon and preserving forest structure is assessed on a regional scale using Global Ecosystem Dynamics Investigation (GEDI) lidar data in combination with a counterfactual analysis using statistical matching. This chapter provides an assessment of the reference condition of the biomass carbon storage capacity by one of the most stringent forest management means. The study finds that analyzed PAs in Tanzania possess 24.4% higher biomass densities than their unprotected counterparts and highlights that community-governed PAs are the most effective category of PAs at preserving forest structure and aboveground biomass density (AGBD). In chapter 3, empirical models are developed to link current (2019-2020) AGBD estimates from the GEDI with Landsat (2007-2019) at a regional scale. This will allow both current wall-to-wall biomass mapping and estimation of biomass dynamics across time. We demonstrate the utility of the method by applying it to quantify the AGBD dynamics associated with forest degradation for charcoal production. In chapter 4, the same modeling framework laid out in chapter 3 will be used to derive AGBD trajectories for 27 forest restoration sites across three biomes in East Africa. To assess the effectiveness of and compare Assisted Natural Regeneration (ANR) and Active Restoration (AR) in enhancing forest AGBD growth compared to natural regeneration (NR), we used staggered difference-in-difference (staggered DiD) to analyze the average annual AGBD change. We controlled for pre-intervention AGBD change rate between AR/ANR and NR and estimated the effectiveness with explicit consideration of intervention duration. This study finds that AR and ANR outperform NR during long-term restoration. Using the most suitable restoration interventions in each biome and timeframe, 4% suitable areas could enhance 2.40 ± 0.78 Gt (billion metric tons) forest carbon uptake over 30 years, equivalent to 3.6 years of African-wide emissions. Overall, this dissertation develops remote sensing methodological frameworks for using GEDI data and its fusion with Landsat time series to quantify and monitor forest AGBD. Moreover, by combining remote sensing-derived AGBD dynamics with impact assessment techniques, such as statistical matching and staggered DiD, the dissertation further assesses and compares different conservation and restoration means’ effectiveness in increasing AGBD and carbon uptake in forests. The dissertation therefore advances the applications of state-of-the-art remote sensing data and techniques for sustainably managing forests towards climate mitigation targets.Item Advances in Mapping Forest Biomass and Old-Growth Conditions Using Waveform Lidar(2023) Bruening, Jamis; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne waveform lidar sys- tem that has transformed scientific understanding of the world’s forests through billions of pre- cise measurements of ecosystem structure. Relative to forest processes that operate on decadal to millennial timescales, the four year period during which GEDI collected these measurements is short, and GEDI’s ability to analyze how forest structure changes over time is mostly unproven. However, fusion efforts that integrate GEDI data with forest inventory measurements and ecosys- tem models hold immense potential for discovery. In this dissertation, I explore the limitations and capabilities of GEDI data for inference into structural and successional dynamics within east- ern US forests. First, I used a forest gap model to quantify uncertainty in biomass predictions for individual GEDI waveforms, and discovered a relationship between biomass uncertainty and successional stage. Next, I investigated uncertainties and errors in large-scale GEDI biomass estimates relative to unbiased estimates from the US forest inventory. I developed a novel mod- eling framework based on fusion between GEDI and the US forest inventory data that corrected these errors, and I produced unbiased and precise maps of forest biomass for the continental US. Lastly, I assessed GEDI’s ability to identify and map different types of old-growth forests, and discovered that GEDI can detect some old forests more effectively than others. This research identified key limitations associated with using GEDI to study forest dynamics, and I leveraged these discoveries to develop new ways of using GEDI data for ecological and successional in- ference. These discoveries will inform new uses of GEDI data and its integration with inventory data and ecosystem modeling to better characterize changes within forest ecosystems.Item Characterizing tree species diversity in the tropics using full-waveform lidar data(2019) Marselis, Suzanne; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Tree species diversity is of paramount value to maintain forest health and to ensure that forests are able to provide all vital functions, such as creating oxygen, that are needed for mankind to survive. Most of the world’s tree species grow in the tropical region, but many of them are threatened with extinction due to increasing natural and human-induced pressures on the environment. Mapping tree species diversity in the tropics is of high importance to enable effective conservation management of these highly diverse forests. This dissertation explores a new approach to mapping tree species diversity by using information on the vertical canopy structure derived from full-waveform lidar data. This approach is of particular interest in light of the recently launched Global Ecosystem Dynamics Investigation (GEDI), a full-waveform spaceborne lidar. First, successful derivation of vertical canopy structure metrics is ensured by comparing canopy profiles from airborne lidar data to those from terrestrial lidar. Then, the airborne canopy profiles were used to map five successional vegetation types in Lopé National Park in Gabon, Africa. Second, the relationship between vertical canopy structure and tree species richness was evaluated across four study sites in Gabon, which enabled mapping of tree species richness using canopy structure information from full-waveform lidar. Third, the relationship between canopy structure and tree species richness across the tropics was established using field and lidar data collected in 16 study sites across the tropics. Finally, it was evaluated how the methods and applications developed here could be adapted and used for mapping pan-tropical tree species diversity using future GEDI lidar data products.Item Novel Applications in Wetland Soils Mapping on the Delmarva Coastal Plain(2018) Goldman, Margaret Anne; Needelman, Brian A; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)On the Delmarva Peninsula, depressional wetlands provide a range of ecosystem services, including water purification, groundwater recharge, provision of critical habitat, and carbon storage. Concern for the health of the Chesapeake Bay and the establishment of the Bay Total Maximum Daily Load have led to growing interest in restoring depressional and other wetland types to mitigate agricultural nitrogen inputs. The ability of natural resource managers to implement wetland restoration to address nonpoint source pollution is constrained by limited spatial information on hydrogeologic and soil conditions favoring nitrogen removal. The goal of this study was to explore the potential of new digital soil mapping techniques to improve identification of wetland soils and map soil properties to improve assessment of wetland ecosystem services, including removing excess nitrogen, and inform natural resource decision making. Previous research on digital soil mapping has focused largely on the development of medium to low-resolution general purpose soil maps in areas of heterogeneous topography and geomorphology. This study was unique in its focus on mapping wetland soils to support wetland restoration decisions in a low relief landscape. A digital soil mapping approach involving the spatial disaggregation of soil data map units was used to create maps of natural soil drainage and texture class. The study was conducted in the upper part of the Choptank River Watershed on central Delmarva, where depressional wetlands occur in high densities and historical loss of wetlands is estimated to be high compared to similar Maryland watersheds. The soil disaggregation techniques developed in this study were successful in creating a more refined representation of natural soil drainage and texture class in forested depressional wetlands. Comparison of the disaggregated soils map with recently developed time-series inundation maps of the region demonstrate the need for further research to understand how indicators of historic and current hydrologic conditions can guide operational soils and wetland mapping and inform wetland restoration decisions.Item Interaction Between the Aerosol Direct Effect in the Lower Troposphere and the Planetary Boundary Layer(2015) Sawyer, Virginia Ruth; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The planetary boundary layer (PBL) limits the vertical mixing of aerosol emitted to the lower troposphere. The PBL depth and its change over time affect weather, surface air quality and radiative forcing. While model simulations have suggested that the column optical properties of aerosol are associated with changes in the PBL depth in turn, there are few long-term measurements of PBL depth with which to validate the theory. Of the existing methods to detect the PBL depth from atmospheric profiles, many require supporting information from multiple instruments or cannot adapt to changing atmospheric conditions. This study combines two common methods for PBL depth detection (wavelet covariance and iterative curve-fitting) in order to produce more reliable PBL depths for micropulse lidar backscatter (MPL). The combined algorithm is also flexible enough to use with radiosonde and atmospheric emitted radiance interferometer (AERI) data. PBL depth retrievals from these three instruments collected at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are compared to one another to show the robustness of the algorithm. The comparisons were made for different times of day, four seasons, and variable sky conditions. While considerable uncertainties exist in PBL detection using all three types of measurements, the agreement among the PBL products is promising, and the different measurements have complementary advantages. The best agreement in the seasonal cycle occurs in winter, and the best agreement in the diurnal cycle when the boundary-layer regime is mature and changes slowly. PBL depths from instruments with higher temporal resolution (MPL and AERI) are of comparable accuracy to radiosonde-derived PBL depths. The new PBL depth measurements for SGP are compared to MPL-derived PBL depths from a multiyear lidar deployment at the Hefei Radiation Observatory (HeRO), and the column aerosol optical depth (AOD) for each site is considered. A one-month period at SGP is also modeled to relate AOD to PBL depth. These comparisons show a weak inverse relationship between AOD and daytime PBL depth. This is consistent with predictions that aerosol suppresses surface convection and causes shallower PBLs.Item AN INVESTIGATION OF CIRRUS CLOUD PROPERTIES USING AIRBORNE LIDAR(2014) Yorks, John E.; Dickerson, Russell R; McGill, Matthew J; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The impact of cirrus clouds on the Earth's radiation budget remains a key uncertainty in assessing global radiative balance and climate change. Composed of ice, and located in the cold upper troposphere, cirrus clouds can cause large warming effects because they are relatively transmissive to short-wave solar radiation, but absorptive of long wave radiation. Our ability to model radiative effects of cirrus clouds is inhibited by uncertainties in cloud optical properties. Studies of mid-latitude cirrus properties have revealed notable differences compared to tropical anvil cirrus, likely a consequence of varying dynamic formation mechanisms. Cloud-aerosol lidars provide critical information about the vertical structure of cirrus for climate studies. For this dissertation, I helped develop the Airborne Cloud-Aerosol Transport System (ACATS), a Doppler wind lidar system at NASA Goddard Space Flight Center (GSFC). ACATS is also a high spectral resolution lidar (HSRL), uniquely capable of directly resolving backscatter and extinction properties of a particle from high-altitude aircraft. The first ACATS science flights were conducted out of Wallops Island, VA in September of 2012 and included coincident measurements with the Cloud Physics Lidar (CPL) instrument. In this dissertation, I provide an overview of the ACATS method and instrument design, describe the ACATS retrieval algorithms for cloud and aerosol properties, explain the ACATS HSRL retrieval errors due to the instrument calibration, and use the coincident CPL data to validate and evaluate ACATS cloud and aerosol retrievals. Both the ACATS HSRL and standard backscatter retrievals agree well with coincident CPL retrievals. Mean ACATS and CPL extinction profiles for three case studies demonstrate similar structure and agree to within 25 percent for cirrus clouds. The new HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions. Furthermore, extinction and particle wind velocity retrieved from ACATS can be used for science applications such as dust transport and convective anvil outflow. The relationship between cirrus cloud properties and dynamic formation mechanism is examined through statistics of CPL cirrus observations from more than 100 aircraft flights. The CPL 532 nm lidar ratios (also referred to as the extinction to backscatter ratio) for cirrus clouds formed by synoptic-scale uplift over land are lower than convectively-generated cirrus over tropical oceans. Errors in assuming a constant lidar ratio can lead to errors of ~50% in cloud optical extinction derived from space-borne lidar such as CALIOP. The 1064 nm depolarization ratios for synoptically-generated cirrus over land are lower than convectively-generated cirrus, formed due to rapid upward motions of tropical convection, as a consequence of differences in cloud temperatures and ice particle size and shape. Finally, the backscatter color ratio is directly proportional to depolarization ratio for synoptically-generated cirrus, but not for any other type of cirrus. The relationships between cirrus properties and formation mechanisms determined in this study can be used as part of a larger global climatology of cirrus clouds to improve parameterizations in global climate models and satellite retrievals to improve our understanding of the impact of clouds on weather and climate.Item MEASURING AND MAPPING FOREST WILDLIFE HABITAT CHARACTERISTICS USING LIDAR REMOTE SENSING AND MULTI-SENSOR FUSION(2005-12-05) Hyde, Peter; Dubayah, Ralph O.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Managing forests for multiple, often competing uses is challenging; managing Sierra National Forest's fire regime and California spotted owl habitat is difficult and compounded by lack of information about habitat quality. Consistent and accurate measurements of forest structure will reduce uncertainties regarding the amount of habitat reduction or alteration that spotted owls can tolerate. Current methods of measuring spotted owl habitat are mostly field-based and emphasize the important of canopy cover. However, this is more because of convenience than because canopy cover is a definitive predictor of owl presence or fecundity. Canopy cover is consistently and accurately measured in the field using a moosehorn densitometer; comparable measurements can be made using airphoto interpretation or from examining satellite imagery, but the results are not consistent. LiDAR remote sensing can produce consistent and accurate measurements of canopy cover, as well as other aspects of forest structure (such as canopy height and biomass) that are known or thought to be at least as predictive as canopy cover. Moreover, LiDAR can be used to produce maps of forest structure rather than the point samples available from field measurements. However, LiDAR data sets are expensive and not available everywhere. Combining LiDAR with other, remote sensing data sets with less expensive, wall-to-wall coverage will result in broader scale maps of forest structure than have heretofore been possible; these maps can then be used to analyze spotted owl habitat. My work consists of three parts: comparison of LiDAR estimates of forest structure with field measurements, statistical fusion of LiDAR and other remote sensing data sets to produce broad scale maps of forest structure, and analysis of California spotted owl presence and fecundity as a function of LiDAR-derived canopy structure. I found that LiDAR was able to replicate field measurements accurately. Additionally, I was able to statistically combine LiDAR with passive optical and RaDAR (SAR backscatter and InSAR range) data to produce broad scale maps of forest structure that are consistent and accurate relative to field data and LiDAR data alone. Finally, I was able to demonstrate that these forest structural attributes predict spotted owl presence and absence as well as productivity.Item Canopy Fuels Inventory and Mapping Using Large-Footprint Lidar(2005-12-05) Peterson, Birgit; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation explores the efficacy of large-footprint, waveform-digitizing lidar for the inventory and mapping of canopy fuels for utilization in fire behavior simulation models. Because of its ability to measure the vertical structure of forest canopies lidar is uniquely suited among remote sensing instruments to observe the canopy structure characteristics relevant to fuels characterization and may help address the lack of high-quality fuels data for many regions, especially in more remote areas. Lidar data were collected by the Laser Vegetation Imaging Sensor (LVIS) over the Sierra National Forest in California. Various waveform metrics were calculated from the waveforms. Field data were collected at 135 plots co-located with a subset of the lidar footprints. The field data were used to calculate ground-based observations of canopy bulk density (CBD) and canopy base height (CBH). These observed values of CBD and CBH were used as dependent variables in a series of regression analyses using the derived lidar metrics as independent variables. Comparisons of observed and predicted resulted in an r2 of 0.71 for CBD and an r2 of 0.59 for CBH. These regression models were then used to generate grids of CBD and CBH from all of the lidar waveform data in the study area. These grids, along with lidar-derived grids of canopy height, were then used as inputs to the FARSITE (Fire Area Simulator-Model) fire behavior model in a series of simulations. Comparisons between conventionally derived and lidar-based model inputs showed differences between the two sets of data. Specifically, the lidar-derived inputs contained much more spatial heterogeneity. Outputs from FARSITE using the lidar-derived inputs were also compared to outputs using input maps of CBD and CBH generated from field observations. There were significant differences between the two sets of outputs, especially in the frequency and spatial distribution of crown fire. Experiments in manipulating the effective resolution of the lidar-based inputs confirmed that FARSITE outputs are affected by the spatial variability of the input data. Furthermore, a sensitivity analysis demonstrated that FARSITE is sensitive to potential errors in the canopy structure input grids. The results of this dissertation show that lidar can be used effectively to predict CBD and CBH for the purpose of fire behavior modeling and that investment in these lidar-based canopy structure data is worthwhile, especially for forests characterized by significant heterogeneity. This work affirms that lidar is a useful tool for future canopy fuels inventory and mapping.